We introduce a novel method to discover beneficial time frames for adapting virtual machine (VM) assignments in consolidated enterprise data centers. Our key insight lies in learning an optimal orthonormal transform from the workload data of a set of enterprise applications hosted in VMs. The transform allows us to extract only a few indicators from long, time-varying and complex workload time series. The indicators represent the initially high-dimensional data set in a reduced form which allows for a concise identification of periods of relatively stable resource demands and turning points in the behavior of a set of VM workloads that require VM reassignments. In this work, we address one of the most pressing problems for data center operators, namely the reduction of managerial complexity of resource and workload management in data centers hosting thousands of applications with complex and varying workload behaviors. We demonstrate the decision support model using workload traces from a professional data center.